Trading Stock Research Articles

Informed Trading of Sell-Side Analysts: Evidence from Class Action Lawsuits

<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines whether sell-side analyst-affiliated investors trade stocks before their analysts release material information. Using class action lawsuits, I explore the pre-lawsuit periods and investigate how potentially informed analysts trade their covered firms. The event study finds that analyst-affiliated investors reduce their stockholdings of firms prior to their own analyst downgrades. The findings are more pronounced among investors employed by investment banks. The post-trading performance of analyst-affiliated investors suggests that they have superior information and front-run to maximize benefits.</span></p>

Taxing Zero

“Zero-price” transactions—in which goods or services are provided at a cash price of zero—are an increasingly important feature of economic life. Consumers can search the web, use email, listen to music and even trade stocks, all without paying anything out of pocket. But zero-price transactions are not free: for-profit businesses provide products at zero price because they get something valuable from consumers in return. Consumers pay with their time, attention or private information. Zero-price transactions are not giveaways; they are a form of barter exchange. Under federal income tax principles, barter exchanges are taxable: both parties to the transaction are taxed on the value they receive. This Article considers the treatment of zero-price transactions from an income tax perspective. Should zero-price transactions be taxed like barter exchanges? If so, how should the amount received in a zero-price transaction be valued? And how can the federal government practically collect tax owed on zero-price transactions?

Informed traders, beauty contest and stock price volatility: Evidence from laboratory markets

AbstractWhether informed investors (e.g., professionals) trade stocks based on the fundamentals or ride on market sentiment is an empirical question, but few attempts have been made due to the lack of field data. We, therefore, conduct a laboratory experiment and examine if informed investors trade stocks based on their anticipation of less‐informed investors' valuation. In our experimental market, informed traders know the fundamental value of the stock; less‐informed traders do not know the accurate value and their valuation may be biased upwards or downwards. We find that informed traders ride on the (mis)valuations of less‐informed traders, and the speculative trades of the informed traders significantly affect stock prices. Our experimental result explains why stock prices are subject to the mass psychology sentiment even in markets with informed investors.

Trading Stocks Builds Financial Confidence and Compresses the Gender Gap

Abstract Many studies document low rates of financial literacy and suboptimal levels of participation in financial markets. These issues are particularly acute among women. Does this reflect a self-reinforcing trap? If so, can a nudge to participate in financial markets generate knowledge, confidence and further increase informed participation? We conduct a large field experiment that enables and incentivises working-age men and women—a challenging group to reach with standard financial training programs—to trade stocks for four to seven weeks. We provide no additional educational content. We find that trading significantly improves financial confidence, as reflected in stock market participation, objective and subjective measures of financial knowledge, and risk tolerance. These effects are especially strong among women. Participants also become more self-reliant and consult others less when making financial decisions.

Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction

The literature on stock forecasting using the innovative technique of Artificial Intelligence (AI) has become overwhelming, making it quite challenging for academics and relevant researchers to gain an elaborative, structured, and organised overview of the relevant information. We fill this gap by contributing and conducting a robust bibliometric review on the application of AI innovations for stock market prediction. More specifically, we conducted a bibliometric review by identifying 241 relevant papers related to stock forecasting using AI by taking a quantitative approach. A quantitative approach uses an examination of linked articles to look at the development of research topics and the structure of existing knowledge. We identified five significant themes based on exploratory factor and hierarchical cluster analyses. We posited that successful AI-based models could aid stock traders, brokers, and investors in better decision-making, a task that had previously been fraught with difficulties. Overall, this paper is aimed at benefiting stock traders, brokers, businesses, investors, government, financial institutions, depositories, and banks. This paper concludes with a future research agenda.

Change in Foreign Investors’ Strategy From Value Investment to High Frequency Trading

The recent trading stocks and trading patterns of foreigners are different from those evaluated in the literature. Considering this, this study analyzed the changes in foreign investors’ strategy regarding stocks listed on the Korea Exchange from 2005 to 2022. The main results are as follows. First, the top 10 foreigners in transaction value shifted from intensively investing in blue-chip stocks to diversifying investments in multiple stocks. Second, foreigners who trade more than 1,000 stocks a day on average have become a new leading force. Third, as regulations on high frequency trading in the United States and Europe were strengthened, foreigners are moving to Asia for regulation arbitrage. The study also shows that the leading force has shifted from value investment trading to high frequency trading in the domestic stock market.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance. Key Words: Virtual stock trading, Stock market simulation, Financial literacy, Investment education, Online trading platforms, Stock market knowledge, Risk management, Investment skills, User experience, Financial education, Stock trading simulation, Virtual investing, Educational technology, Portfolio management.

Studying Stock Prediction in the Context of Machine Learning Exemplified by Analyzing SVM and LSTM Models

The impact of stocks on economic development is significant, with most companies employing quantitative finance methods to analyze and trade stocks. In the present day, there is a growing plethora of stock prediction methods, requiring individuals to carefully select the appropriate means of prediction. This study aims to present the highly regarded machine learning algorithms of recent years. Machine learning enables the extraction of trading strategies by learning patterns from historical stock data. Deep learning, a subset of machine learning, eliminates the need for researchers to manually extract features, instead utilizing neural network models that have proven effective for stock analysis. This paper also provides an extensive analysis of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), elucidating their algorithmic principles and assessing their strengths and weaknesses based on the findings of other researchers. It is observed that SVM is effective for predicting small quantities, but lacks suitability for non-stationary data prediction due to the non-informative nature of financial time series data, which significantly diminishes the accuracy of SVM predictions. In contrast, LSTM emerges as the most comprehensive single algorithm, exhibiting high accuracy and efficiency across all aspects. The paper also explores advanced hybrid models, which demonstrate that combining the advantages of multiple models can lead to higher accuracy and efficiency. The future of stock prediction algorithms holds promise for development, with the potential to construct an intelligent system that automatically selects data for analysis.

Outcome-Based Education

The literature laments college students’ lack of financial literacy and their lackadaisical interest in learning. However, our research demonstrates that students can be enticed such that they get interested in acquiring financial skills. Generally, college students live hand-to-mouth and are happy to learn about money-making opportunities (not necessarily financial management). Students were informed that qualified faculty will conduct a series of workshops whereby they will learn financial tools and skills and will also get an opportunity to trade stocks and options first-hand. Literature on pedagogy confirms that experiential learning is the best way to learn. Students also prefer practical, hands-on learning more than listening to a lecture or reading a book. Moreover, if workshops (for which professionals charge exorbitant sums) are offered for free, students’ interest soars, and they enrol in hordes. This is confirmed by our workshops which proved successful in enhancing student interest and imparting financial skills. The workshops were innovative and unique as they were online, interactive, tutored, and free. They were open to students from all majors, faculty, and staff. Students are excited about stock trading but are generally apprehensive of and intimidated by financial options. This was true of our sample of students as well, but the faculty-led, friendly, and riskless environment of hands-on learning provided a great opportunity, and over 100 individuals enrolled in our workshops. A comparison of pre- and post-survey quizzes shows that participants’ interest in, and knowledge of options trading increased significantly due to the workshops.

A Story of Russia-Ukraine War and its effects on Indian Economy

The repercussions of Russia-Ukraine war are threatening the global economy, shaking financial markets and making life more perilous for everyone across the world. Despite India’s limited direct exposure, the combination of supply disruptions and ongoing terms of trade stock will likely weigh on growth, result in a sharper rise in inflation and (lead to) a wider current account deficit and a fallen rupee said Sonal Verma chief economist at Nomura Holdings in a report. As supply disruptions mounted following sanctions on Russian Banks, oil prices surged. The Global economy was straining under a range of burdens, surging inflation, tangled supply chains and tumbling stock prices.

Comparison of XGBoost and LSTM Models for Stock Price Prediction

Along with the development of technology, machine learning would take up a higher role in analyzing categories. Among those categories, predicting stock price meets the needs of most peopleor most people who trade stocks. By referring to the predicting model, stock traders can decide whether they should trade in or trade out to make a profit in the stock market. Therefore, it is necessary to testify which model can make the prediction with higher accuracy. To analyze this problem, this article examines the performance of different models under different size of datasets. This paper compared XGBoost and LSTM model by collecting stock price data that are 3 years, 6 years, and 9 years ago from the year 2023. Then analyze the close price of stock prices those models. By comparing the figures and calculated rmse value in each year and each model, the impact of different dataset sizes on each model would be revealed. This paper discovered that XGBoost model has greater accuracy under large-size dataset overall, but LSTM can predict more accurate stock price under small-size dataset.

Construction of International Trade and Investment Platform Based on Artificial Intelligence Technology

Abstract This paper completes the implementation of the risk control module of the international trade investment decision-making module through the construction process of the programmed international trade investment platform. Combining the decision tree algorithm and logistic regression algorithm to categorize and screen the factors affecting the fluctuation of international trade stock prices to obtain the international trade market fundamentals and technical factors that have a significant impact on international trade stock prices. The Kalman filter model is applied to improve the effective market factors screened so that the factor selection of the international trade investment model is more advantageous. After constructing the platform, in order to reflect its convenience, the RE, OLS, TI, TVD and BC models were used to analyze the export trade efficiency of the home country, respectively. The results show that the degree of facilitation of the host country’s trade and investment platform is positively correlated with the loss of the home country’s export trade efficiency (0.043), which is statistically significant at the 1% significance level, and that the loss of the home country’s export trade efficiency increases by 4.3% for every 1 unit increase in the degree of facilitation of the host country’s trade and investment platform. The construction of the international trade and investment platform increases the facilitation of export trade from trading partner countries to the host country, which indirectly increases the export trade efficiency loss of the home country.

INFORMATION SOURCES OF TRADING DECISION MAKING AS DETERMINING FACTORS FOR COGNITIVE BIASES

In the article the issues of trading decision making are being discussed in the lights of cognitive biases in uncertainty. The process of trading stocks requires buy/sell decision making in uncertain conditions under extreme time constraints. In ever-changing market decisions are made momentarily and cognitive biases and heuristics determine the speed and quality of decisions. Information and expectations are the core of cognitive component of the process and can influence biases.

Neurofinance: Exploratory Analysis Stock Trader's Decision-Making Process by Real-Time Monitoring of Emotional Reactions

Human emotions can be associated with decision-making, and emotions can generate behaviors. Due to the fact that it could be biased and exhaustively complex to examine how human beings make choices, it is necessary to consider relevant groups of study, such as stock traders and non-traders in finance. This work aims to analyze the connection between emotions and the decision-making process of investors and non-investors submitted to the same set of stimuli to understand how emotional arousal might dictate the decision process. Neuroscience monitoring tools such as Real-Time Facial Expression Analysis (AFFDEX), Eye-Tracking, and Galvanic Skin Response (GSR) were adopted to monitor the related experiments of this paper and its accompanying analysis process. Thirty-seven participants attended the study, 24 were classified as stock traders, and 13 were non-traders; the mean age for the groups was 35 and 25, respectively. The designed experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. On the contrary, investors were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. Future works may consider expanding the study to larger sample size, different demographic groups, and other biometrics for further analysis and conclusions.

LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry

The launch of ChatGPT has overwhelmingly been revolutionizing the stock market. Of particular interests of stock traders and financial analysts, discovery about artificial intelligence stock market has become the main focus. The paper selected top worldwide artificial intelligence (AI) enterprises from Yahoo Finance and made future return forecasts with the long short-term memory networks (LSTM). The predicted information is employed in conducting portfolio optimization within the scope of mean-variance analysis to obtain an assessment of the portfolios performance. The outcomes illustrate that the utilization of the LSTM model exhibits aptness in forecasting the forthcoming returns of financial instruments. Furthermore, a favorable preference entails the inclusion of NVDIA and Microsoft stocks in the portfolio. These discoveries offer utility in proposing pioneering investment strategies and aligning with the prevailing tendencies of societal progression.

Pari-Mutuel Betting Markets: Racetracks and Lotteries Revisited

This survey discusses the state of the art in research in racetrack and lottery investment markets. Market efficiency and the pricing of various wagers are studied along with new developments since the Thaler & Ziemba (1988) review. The weak form inefficient market pricing approach using stochastic programming optimization models changed racetrack betting from handicapping to a financial market allowing professional syndicates to operate as hedge funds. Topics discussed include arbitrage and risk arbitrage, syndicates, betting exchange rebates, behavioral biases, and fundamental and mispricing information in racetrack and lottery markets. Similar models can be used to successfully trade stock market anomalies. Supplemental Materials are included online.

Trauma and investment horizon: Evidence from a representative China equity investor behavior survey

Results from a large sample of individual Chinese investors demonstrate that they were more likely to trade stocks for short-term speculation after experiencing trauma such as natural disasters, serious illness, or death in their immediate family. They exhibited higher impulsivity, a greater desire for immediate gratification, a greater willingness to follow trends, and more risk-taking behaviors as a result of the trauma experience. The data also show that the negative relation between trauma experience and investment horizon is less pronounced for older and married individuals.

Development of Trading Strategies Using Time Series Based on Robust Interval Forecasts

The task of time series forecasting is to estimate future values based on available observational data. Prediction Intervals methods are aimed at finding not the next point, but the interval that the future value or several values on the forecast horizon can fall into given current and historical data. This article proposes an approach for modeling a robust interval forecast for a stock portfolio. Here, a trading strategy was developed to profit from trading stocks in the market. The study used real trading data of real stocks. Forty securities were used to calculate the IMOEX. The securities with the highest weight were the following: GAZP, LKOH, SBER. This definition of the strategy allows operating with large portfolios. Increasing the accuracy of the forecast was carried out by estimating the interval of the forecast. Here, a range of values was considered to be a result of forecasting without considering specific moments, which guarantees the reliability of the forecast. The use of a predictive interval approach for the price of shares allows increasing their profitability.